Sonar Inspired Optimization In Energy Problems Related To Load And Emission Dispatch

One of the upcoming categories of Computational Intelligence (CI) is meta-heuristic schemes, which derive their intelligence from strategies that are met in nature, namely Nature Inspired Algorithms. These algorithms are used in various optimization problems because of their ability to cope with multi-objective problems and solve difficult constraint optimization problems. In this work, the performance of Sonar Inspired Optimization (SIO) is tested in a non-smooth, nonconvex multi-objective Energy problem, namely the Economic Emissions Load Dispatch (EELD) problem. The research hypothesis was that this new nature-inspired method would provide better solutions because of its mechanisms. The algorithm manages to deal with constraints, namely Valve-point Effect and Multi-fuel Operation, and produces only feasible solutions, which satisfy power demand and operating limits of the system examined. Also, with a lot less number of agents manages to be very competitive against other meta-heuristics, such as hybrid schemes and established nature inspired algorithms. Furthermore, the proposed scheme outperforms several methods derived from literature.